2023 - Research.com Computer Science in China Leader Award
Liang Gao focuses on Mathematical optimization, Job shop scheduling, Flow shop scheduling, Algorithm and Metaheuristic. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Scheduling, Dynamic priority scheduling and Benchmark. His study looks at the relationship between Job shop scheduling and fields such as Genetic algorithm, as well as how they intersect with chemical problems.
His Flow shop scheduling study which covers Hybrid algorithm that intersects with Automated planning and scheduling. His work deals with themes such as Failure probability, Design of experiments, Kriging, Upper and lower bounds and Robustness, which intersect with Algorithm. In his work, Machining is strongly intertwined with Optimization problem, which is a subfield of Metaheuristic.
Liang Gao mostly deals with Mathematical optimization, Algorithm, Job shop scheduling, Artificial intelligence and Scheduling. His Mathematical optimization study integrates concerns from other disciplines, such as Scheduling and Flow shop scheduling. His Flow shop scheduling study results in a more complete grasp of Dynamic priority scheduling.
As part of the same scientific family, Liang Gao usually focuses on Algorithm, concentrating on Benchmark and intersecting with Differential evolution. His Job shop scheduling research is multidisciplinary, incorporating elements of Energy consumption, Efficient energy use and Metaheuristic. His studies in Artificial intelligence integrate themes in fields like Fault, Machine learning and Pattern recognition.
His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Job shop scheduling, Topology optimization and Algorithm. Liang Gao studied Mathematical optimization and Kriging that intersect with Active learning. His Artificial intelligence research includes themes of Fault, Machine learning and Pattern recognition.
His Job shop scheduling research incorporates themes from Energy consumption, Scheduling, Metaheuristic and Heuristic. Liang Gao has researched Topology optimization in several fields, including Topology, Interpolation, Topology and Homogenization. Liang Gao interconnects Optimization problem and Benchmark in the investigation of issues within Evolutionary algorithm.
Mathematical optimization, Topology optimization, Algorithm, Job shop scheduling and Artificial intelligence are his primary areas of study. His research on Mathematical optimization frequently connects to adjacent areas such as Interval. His Algorithm research includes elements of Swarm behaviour, Mode, Surrogate model and Confidence interval.
His Job shop scheduling study incorporates themes from Linear programming and Energy consumption. His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition. The various areas that Liang Gao examines in his Flow shop scheduling study include Scheduling and Efficient energy use.
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A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Long Wen;Xinyu Li;Liang Gao;Yuyan Zhang.
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
Long Wen;Liang Gao;Xinyu Li.
An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem
Guohui Zhang;Xinyu Shao;Peigen Li;Liang Gao.
An effective genetic algorithm for the flexible job-shop scheduling problem
Guohui Zhang;Liang Gao;Yang Shi.
An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem
Xinyu Li;Liang Gao.
Integration of process planning and scheduling-A modified genetic algorithm-based approach
Xinyu Shao;Xinyu Li;Liang Gao;Chaoyong Zhang.
An improved fruit fly optimization algorithm for continuous function optimization problems
Quan-Ke Pan;Quan-Ke Pan;Hong-Yan Sang;Jun-Hua Duan;Liang Gao.
Cellular particle swarm optimization
Yang Shi;Hongcheng Liu;Liang Gao;Guohui Zhang.
Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm
Chao Lu;Liang Gao;Xinyu Li;Quanke Pan.
A transfer convolutional neural network for fault diagnosis based on ResNet-50
Long Wen;Xinyu Li;Liang Gao.
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